Detecting clinician implicit biases in diagnoses using proximal causal inference
This addresses the issue of systemic discrimination in healthcare for marginalized groups by providing a tool to measure biases from observational data, though it is incremental as it builds on existing causal methods.
The authors tackled the problem of detecting clinician implicit biases in diagnoses using a causal inference approach on large-scale medical data, specifically applying proximal mediation to UK Biobank data to disentangle effects of sociodemographic attributes on diagnosis decisions.
Clinical decisions to treat and diagnose patients are affected by implicit biases formed by racism, ableism, sexism, and other stereotypes. These biases reflect broader systemic discrimination in healthcare and risk marginalizing already disadvantaged groups. Existing methods for measuring implicit biases require controlled randomized testing and only capture individual attitudes rather than outcomes. However, the "big-data" revolution has led to the availability of large observational medical datasets, like EHRs and biobanks, that provide the opportunity to investigate discrepancies in patient health outcomes. In this work, we propose a causal inference approach to detect the effect of clinician implicit biases on patient outcomes in large-scale medical data. Specifically, our method uses proximal mediation to disentangle pathway-specific effects of a patient's sociodemographic attribute on a clinician's diagnosis decision. We test our method on real-world data from the UK Biobank. Our work can serve as a tool that initiates conversation and brings awareness to unequal health outcomes caused by implicit biases.